Dave Epstein
Impact in
- Software top 5%
- Software Testing and Debugging Techniques
- Software Reliability and Analysis Research
- Signal Processing top 10%
- Advanced Malware Detection Techniques
Papers in
-
- Multimodal Machine Learning Applications 4
- Human Pose and Action Recognition 2
- Advanced Image and Video Retrieval Techniques 1
-
- Software Engineering Research 2
- Co-authors
- Dongdong She (2 shared papers)Baishakhi Ray (2 shared papers)Suman Jana (2 shared papers)Junfeng Yang (2 shared papers)Kexin Pei (2 shared papers)Carl Vondrick (2 shared papers)Dídac Surís (2 shared papers)Heng Ji (1 shared paper)
- Journals
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)arXiv (Cornell University) (3 papers)
- Partner nations
- United States
In The Last Decade
Dave Epstein
6 papers receiving 135 citations
Peers
Comparison fields: 5 of 30
- Software 92
- Signal Processing 66
- Information Systems 51
- Hardware and Architecture 12
- Artificial Intelligence 46
Countries citing papers authored by Dave Epstein
This map shows the geographic impact of Dave Epstein's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dave Epstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dave Epstein more than expected).
Fields of papers citing papers by Dave Epstein
This network shows the impact of papers produced by Dave Epstein. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dave Epstein. The network helps show where Dave Epstein may publish in the future.
Co-authors
The 14 scholars most cited alongside Dave Epstein, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 106 | |
| 2 | NEUZZ: Efficient Fuzzing with Neural Program Learning | 2018 | 15 |
| 3 | 2021 | 10 | |
| 4 | 2022 | 5 | |
| 5 | Learning to Learn Words from Narrated Video. | 2019 | 3 |
| 6 | Video Representations of Goals Emerge from Watching Failure. | 2020 | 1 |
| 7 | 2023 | 1 |
About Dave Epstein
Dave Epstein is a scholar working on Computer Vision and Pattern Recognition, Information Systems, Signal Processing, Artificial Intelligence and Software, having authored 7 papers that have together received 141 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (4 papers), Software Testing and Debugging Techniques (2 papers), Software Engineering Research (2 papers), Advanced Malware Detection Techniques (2 papers), Human Pose and Action Recognition (2 papers), Advanced Image and Video Retrieval Techniques (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Natural Language Processing Techniques (1 paper). The work is most often cited by research in Software (92 citations), Signal Processing (66 citations), Information Systems (51 citations), Hardware and Architecture (12 citations) and Artificial Intelligence (46 citations). Dave Epstein has collaborated with scholars based in United States. Frequent co-authors include Dongdong She, Baishakhi Ray, Suman Jana, Junfeng Yang, Kexin Pei, Carl Vondrick, Dídac Surís, Carl Vondrick, Heng Ji and Shih-Fu Chang. Their work appears in journals such as Rare & Special e-Zone (The Hong Kong University of Science and Technology), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and arXiv (Cornell University).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.